What is a social network?

• Nodes
  • Usually people
• Edges / ties
  • Relationships between them
    • Friendship
    • Hatred
    • Advice
    • Time spent together
    • Diseases transmitted

Network Data

• What are the three main ways of representing networks?

Matrices

sw = watts.strogatz.game(2, 4, 1, .2)
as_adjacency_matrix(sw)

## 16 x 16 sparse Matrix of class "dgCMatrix"
##                                      
##  [1,] . 1 . . 1 . . . 1 . . . 1 . 1 .
##  [2,] 1 . 1 . . . . . . . . . . 1 1 1
##  [3,] . 1 . 1 . . 1 . . . . . . . . 1
##  [4,] . . 1 . . . . 1 . . . . . . . .
##  [5,] 1 . . . . 1 . 1 1 . . . . . . .
##  [6,] . . . . 1 . . . . 1 . . . . . .
##  [7,] . . 1 . . . . 1 1 . 1 . . . . .
##  [8,] . . . 1 1 . 1 . . . . 1 . . . .
##  [9,] 1 . . . 1 . 1 . . 1 . 1 1 . 1 .
## [10,] . . . . . 1 . . 1 . 1 . . 1 1 .
## [11,] . . . . . . 1 . . 1 . . . 1 1 .
## [12,] . . . . . . . 1 1 . . . . . . .
## [13,] 1 . . . . . . . 1 . . . . . . 1
## [14,] . 1 . . . . . . . 1 1 . . . 1 .
## [15,] 1 1 . . . . . . 1 1 1 . . 1 . .
## [16,] . 1 1 . . . . . . . . . 1 . . .

Edgelists

as_edgelist(sw)

##       [,1] [,2]
##  [1,]    1    2
##  [2,]    1    5
##  [3,]    2    3
##  [4,]    2   15
##  [5,]    3    4
##  [6,]    3    7
##  [7,]    1   15
##  [8,]    4    8
##  [9,]    5    6
## [10,]    5    9
## [11,]    7    9
## [12,]    6   10
## [13,]    7    8
## [14,]    7   11
## [15,]    5    8
## [16,]    8   12
## [17,]    9   10
## [18,]    9   13
## [19,]   10   11
## [20,]   10   14
## [21,]    1    9
## [22,]   11   15
## [23,]    9   12
## [24,]    3   16
## [25,]   11   14
## [26,]    1   13
## [27,]   14   15
## [28,]    2   14
## [29,]   10   15
## [30,]    9   15
## [31,]   13   16
## [32,]    2   16

Graphs/plots/sociograms

plot(sw,
     vertex.label.cex = .5
     )

Where does social network data come from?

• Surveys
• Observation
• Trace data

Ego networks

• Typically created from surveys

plot(make_ego_graph(sw, nodes = 1)[[1]])

Extended ego networks

Bipartite Networks

• 20 random reddittors and their communities

What does this data look like?

head(r)

## # A tibble: 6 x 3
##   author         subreddit           posts
##   <chr>          <chr>               <dbl>
## 1 furiousxgeorge eagles                 64
## 2 furiousxgeorge PoliticalDiscussion    55
## 3 furiousxgeorge OurPresident           29
## 4 greihund       embedded                1
## 5 yungbelle1999  JUSTNOFAMILY            1
## 6 greihund       Sourdough               1

Bipartite networks can be “projected”

• People who post in the same communities

Communities with the same users

Nodes and edges can have attributes

Edge attributes

• Usually this is weight

What can we learn from networks?

• Metrics about the network
  • Density, clustering, etc.
• Metrics about the nodes
  • Power, influence, popularity
• Look at how things change over time

Today’s Dad Joke

Shout out to people wondering what the opposite of ‘in’ is.

Housekeeping

• Discussion questions
  • Monday at noon

Schedule

• Housekeeping / Announcements (5-10)
• Discussion and review (35-45)
• Consolidation and Confusion (10-15)
• Supplementary lectures
  • Response to needs
  • Discuss at end of class

Review

• How did installing R + RStudio go?
• Homework review
  • Encouragement to be prepared
  • Time to identify confusion - it’s OK to be confused!
• Discussion questions review

Consolidation

• What were some of the key ideas?
• What are you thinking differently about now?
• What are remaining questions/confusions?